cavis/libnd4j/include/ops/declarable/generic/parity_ops/lstsq.cpp

133 lines
5.9 KiB
C++

/*******************************************************************************
* Copyright (c) 2020 Konduit, K.K.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// Created by GS <sgazeos@gmail.com> at 01/28/2020
//
#include <op_boilerplate.h>
#if NOT_EXCLUDED(OP_lstsq)
#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/helpers/lstsq.h>
namespace nd4j {
namespace ops {
CUSTOM_OP_IMPL(lstsq, 2, 1, false, 0, 0) {
auto a = INPUT_VARIABLE(0);
auto b = INPUT_VARIABLE(1);
auto z = OUTPUT_VARIABLE(0);
bool fastFlag = true;
double l2_factor = 0.;
if (block.numB() > 0) {
fastFlag = B_ARG(0);
}
if (block.numT() > 0) {
l2_factor = T_ARG(0);
}
REQUIRE_TRUE(a->rankOf() >=2, 0, "lstsq: The rank of input left tensor should not be less than 2, but %i is given", a->rankOf());
REQUIRE_TRUE(b->rankOf() >=2, 0, "lstsq: The rank of input right tensor should not be less than 2, but %i is given", b->rankOf());
// REQUIRE_TRUE(a->sizeAt(-1) == a->sizeAt(-2), 0, "lstsq: The last two dimmensions should be equal, but %i and %i are given", a->sizeAt(-1), a->sizeAt(-2));
REQUIRE_TRUE(a->sizeAt(-2) == b->sizeAt(-2), 0, "lstsq: The last dimmension of left part should be equal to prelast of right part, but %i and %i are given", a->sizeAt(-1), b->sizeAt(-2));
//REQUIRE_TRUE(l2_factor == 0., 0, "lstsq: Implementation of operation is not finished for factor difference from 0.");
if (a->isEmpty() || b->isEmpty() || z->isEmpty())
return Status::OK();
auto res = helpers::leastSquaresSolveFunctor(block.launchContext(), a, b, l2_factor, fastFlag, z);
return res;
}
CUSTOM_OP_IMPL(solve_ls, 2, 1, false, 0, 0) {
auto a = INPUT_VARIABLE(0);
auto b = INPUT_VARIABLE(1);
auto z = OUTPUT_VARIABLE(0);
bool fastFlag = true;
double l2_factor = 0.;
if (block.numB() > 0) {
fastFlag = B_ARG(0);
}
if (block.numT() > 0) {
l2_factor = T_ARG(0);
}
REQUIRE_TRUE(a->rankOf() >=2, 0, "lstsq: The rank of input left tensor should not be less than 2, but %i is given", a->rankOf());
REQUIRE_TRUE(b->rankOf() >=2, 0, "lstsq: The rank of input right tensor should not be less than 2, but %i is given", b->rankOf());
// REQUIRE_TRUE(a->sizeAt(-1) == a->sizeAt(-2), 0, "lstsq: The last two dimmensions should be equal, but %i and %i are given", a->sizeAt(-1), a->sizeAt(-2));
REQUIRE_TRUE(a->sizeAt(-2) == b->sizeAt(-2), 0, "lstsq: The last dimmension of left part should be equal to prelast of right part, but %i and %i are given", a->sizeAt(-1), b->sizeAt(-2));
//REQUIRE_TRUE(l2_factor == 0., 0, "lstsq: Implementation of operation is not finished for factor difference from 0.");
auto res = Status::OK();
if (a->isEmpty() || b->isEmpty() || z->isEmpty())
return res;
res = helpers::leastSquaresSolveFunctor(block.launchContext(), a, b, l2_factor, fastFlag, z);
return res;
}
DECLARE_SYN(MatrixSolveLs, lstsq);
DECLARE_SHAPE_FN(lstsq) {
auto in0 = inputShape->at(0);
auto in1 = inputShape->at(1);
auto shapeOf = ShapeUtils::shapeAsVector(in1);
auto rank = shapeOf.size();
shapeOf[rank - 2] = shape::sizeAt(in0, -1);
if (shape::isEmpty(in0) || shape::isEmpty(in1)) {
shapeOf[rank - 1] = 0; // set output shape to empty
}
auto resShape = ConstantShapeHelper::getInstance()->createShapeInfo(ArrayOptions::dataType(in0), shape::order(in1), shapeOf);//ShapeBuilders::copyShapeInfoAndType(in1, in0, true, block.workspace());
if (shapeOf[rank - 1] == 0) {
ArrayOptions::setPropertyBit(resShape, ARRAY_EMPTY);
}
return SHAPELIST(resShape);
}
DECLARE_TYPES(lstsq) {
getOpDescriptor()
->setAllowedInputTypes({ALL_FLOATS})
->setAllowedOutputTypes({ALL_FLOATS})
->setSameMode(false);
}
DECLARE_SHAPE_FN(solve_ls) {
auto in0 = inputShape->at(0);
auto in1 = inputShape->at(1);
auto shapeOf = ShapeUtils::shapeAsVector(in1);
auto rank = shapeOf.size();
shapeOf[rank - 2] = shape::sizeAt(in0, -1);
if (shape::isEmpty(in0) || shape::isEmpty(in1)) {
shapeOf[rank - 1] = 0; // set output shape to empty
}
auto resShape = ConstantShapeHelper::getInstance()->createShapeInfo(ArrayOptions::dataType(in0), shape::order(in1), shapeOf);//ShapeBuilders::copyShapeInfoAndType(in1, in0, true, block.workspace());
if (shapeOf[rank - 1] == 0) {
ArrayOptions::setPropertyBit(resShape, ARRAY_EMPTY);
}
return SHAPELIST(resShape);
}
DECLARE_TYPES(solve_ls) {
getOpDescriptor()
->setAllowedInputTypes({ALL_FLOATS})
->setAllowedOutputTypes({ALL_FLOATS})
->setSameMode(false);
}
}
}
#endif